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# Sr Machine learning Engineer

- **Company:** Ria Money Transfer
- **Location:** United States (Remote available)
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Amazon Web Services, Continuous Integration, Data Systems, Monitoring of Systems, Python (Programming Language), Machine Learning, Data Streaming, Datadog, Grafana, Apache Spark, Model Validation, Cloudformation, Containerization, Pyspark, Scikit Learn, Kubernetes, Infrastructure Automation Frameworks, Machine Learning Operations, Cloudwatch, Terraform, Docker, Pagerduty, Microservices
- **Published:** October 9, 2026
- **Apply:** https://arc.dev/remote-jobs/j/redirect/ppzzisaycf

## About the Role

* 5+ years of experience as a Machine Learning Engineer, MLOps Engineer, or related role.
 * Proficiency in Python and ML libraries (e.g., scikit-learn,…)
 * Experience with MLOps frameworks and tools
 * Strong understanding of containerization and deployment (Docker, Kubernetes).
 * Proficiency with AWS and IaC tools (Terraform, CloudFormation).
 * Experience with monitoring and observability stacks ( Grafana, CloudWatch, Opsgenie, Datadog).
 * Familiarity with streaming and batch data systems ( Spark, PySpark, Kenises).
 * Excellent problem-solving and communication skills; comfortable working cross-functionally.

## Description

We're looking for a Sr Machine Learning engineer with a strong focus on MLOps to join our growing team. In this role, you'll own the end-to-end infrastructure and automation of ML pipelines, with a focus on deploying, scaling, monitoring, and maintaining real-time and batch machine learning workflows in production environments.

This role collaborates closely with data scientists, IT, and platform teams to ensure ML models are production-ready, reliable, and observable at scale.

Roles & Responsibilities

 * Own, design, build, and maintain real-time and batch ML and pipelines supporting the end-to-end ML lifecycle.
 * Develop infrastructure and tooling for continuous integration, testing, deployment, and retraining of ML models.
 * Implement and operate feature stores, model registries, and orchestration frameworks for reproducible ML workflows.
 * Deploy and serve ML models in low-latency and high-throughput production environments using containerized microservices.
 * Implement robust monitoring systems for Model Performance, Data quality, and Operational health.
 * Build automated alerting and dashboarding for visibility into ML system health.
 * Ensure model traceability, auditability, and governance practices.
 * Optimize infrastructure on cloud platforms (AWS) using Infrastructure as Code.
 * As part of our ML operation team, this role will be responsible to occasionally be on call over the weekends in the events of incidents.